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distributed Estimation Under Sensor Attacks: Linear and Nonlinear Measurement Models

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2021年 7卷 156-165页

作者： Meng, Min Li, Xiuxian Xiao, Gaoxi Tongji Univ Dept Control Sci & Engn Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Inst Advanced Study Shanghai 200092 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper investigates distributed estimation of an unknown vector parameter in adversarial environments. Individual agents make successive local measurements of the unknown parameter and aim at estimating the unknown parameter consistently by sharing these measurements with their neighbors over a time-varying directed communication graph even when some of the agents are under attacks and their measurements are manipulated arbitrarily. To this end, we design push-sum-based recursive algorithms to estimate the unknown parameter for linear and nonlinear measurement models, respectively. It is demonstrated that the presented algorithms can ensure that the local estimates at all the agents converge to the true value of the parameter under some mild assumptions, such as, B-strong-connectedness of the communication topologies and a topology-independent constraint on the number of compromised measurements. A numerical example is presented to illustrate the effectiveness of the proposed algorithms.

关键词： Estimation Time measurement Particle measurements Atmospheric measurements distributed algorithms Topology Robot sensing systems distributed estimation sensor attacks push-sum linear and nonlinear measurement models

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Chance Constrained distributed Optimisation for Interconnected Power Systems ⁎

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IFAC-PapersOnLine 2024年第13期58卷 128-133页

作者： Ineza Remy Mugenga Abebe Geletu Pu Li African Institute of Mathematical Sciences (AIMS) Rwanda P.O. Box: 7150 Kigali Rwanda Group of Process Optimization Institute for Automation and Systems Engineering Technische Universität Ilmenau P.O. Box 100565 98684 Ilmenau Germany

Microgrids have been attracted increasingly attention due to renewable energy integration. Multiple microgrids are often interconnected for benefiting each other. A significant challenge in operating such power systems lies in the uncertainties including intermittence in renewable energy sources and load demands estimation. Sharing state variables in interconnected systems poses additional difficulties. This paper proposes a numerical solution approach to a chance constrained distributed optimisation for interconnected power systems under uncertainty. The inner-outer approximation approach is used for the solution and ALADIN for numerical implementation. An interconnected optimal power flow problem with a three-bus and a four-bus system is solved as a case study.

关键词： Interconnecting microgrids shared state systems distributed algorithms ALADIN chance constraints inner-outer approximation

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distributed approximation for f-matching

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THEORETICAL COMPUTER SCIENCE 2024年 1014卷

作者： Czygrinow, Andrzej Hanckowiak, Michal Ruminski, Andrzej Witkowski, Marcin Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA Adam Mickiewicz Univ Fac Math & Comp Sci Poznan Poland

Let G = (V, E) be a graph and let f: V -> Z(+). An f-matching in G is a set of edges F subset of E such that every vertex v is an element of V is incident to at most f(v) edges. In this paper we will give a constan... 详细信息

关键词： distributed algorithms Matching f-matching LOCAL model

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distributed Prediction of Unsafe Reconfiguration Scenarios of Modular Robotic Programmable Matter

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IEEE TRANSACTIONS ON ROBOTICS 2021年第6期37卷 2226-2233页

作者： Piranda, Benoit Chodkiewicz, Pawel Holobut, Pawel Bordas, Stephane Bourgeois, Julien Lengiewicz, Jakub Univ Bourgogne Franche Comte FEMTO ST Inst CNRS F-25000 Besancon France Warsaw Univ Technol Fac Automot & Construct Machinery Engn PL-02524 Warsaw Poland Polish Acad Sci Inst Fundamental Technol Res Dept Mech Mat PL-02106 Warsaw Poland Univ Luxembourg Fac Sci Technol & Med Dept Engn L-4365 Luxembourg Luxembourg

We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats intermodular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulator VisibleSim, and in real-life experiments on the modular robotic system Blinky Blocks.

关键词： Robots Torque Mathematical model Topology Convergence Computational modeling Bending distributed algorithms modular robots mechanical constraints programmable matter self-reconfiguration

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Convergence and Accuracy Analysis for a distributed Static State Estimator Based on Gaussian Belief Propagation

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第10期66卷 4785-4791页

作者： Marelli, Damian Sui, Tianju Fu, Minyue Sun, Ximing Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China French Argentine Int Ctr Informat & Syst Sci S2000EZP Rosario Argentina Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China Univ Newcastle Callaghan NSW 2308 Australia

This article focuses on the distributed static estimation problem. A belief propagation (BP) based estimation algorithm is studied for its convergence and accuracy. More precisely, we give conditions under which the BP-based distributed estimator is guaranteed to converge and we give concrete characterizations for its accuracy. Our results reveal new insights and properties of this distributed algorithm, leading to better theoretical understanding of static distributed state estimation and new applications of the algorithm.

关键词： Convergence Estimation State estimation Jacobian matrices distributed algorithms Large-scale systems Belief propagation Accuracy analysis belief propagation (BP) convergence analysis distributed state estimation

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distributed Ordinal Regression Over Networks

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IEEE ACCESS 2021年 9卷 62493-62504页

作者： Liu, Huan Tu, Jiankai Li, Chunguang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

Many real-world data are labeled with natural orders, i.e., ordinal labels. Examples can be found in a wide variety of fields. Ordinal regression is a problem to predict ordinal labels for given patterns. There are specially developed ordinal regression methods to tackle this type of problems, but they are usually centralized. However, in some scenarios, data are collected distributedly by nodes of a network. For the purpose of privacy protection or due to some practical constraints, it is difficult or impossible to transmit the data to a fusion center for processing. Thus the centralized ordinal regression methods are inapplicable. In this paper, we formulate a distributed generalized ordered logit model for distributed ordinal regression. To estimate parameters in the model, a distributed constrained optimization formulation based on maximum likelihood methods is established. Then, we propose a projected gradient based algorithm to solve the optimization problem. We prove the consensus and the convergence of the proposed distributed algorithm. We also conduct numerical simulations on synthetic and real-world datasets. Simulation results show that the proposed distributed algorithm is comparable to the corresponding centralized algorithm. Even when the data label distribution among nodes is unbalanced, the proposed algorithm still has competitive performance.

关键词： distributed algorithms Data models distributed databases Standards Numerical models Maximum likelihood estimation Numerical simulation Ordinal regression distributed algorithm generalized ordered logit model maximum likelihood

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distributed Multiobjective Optimization for Network Resource Allocation of Multiagent Systems

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 5800-5810页

作者： Li, Zhongguo Ding, Zhengtao Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England

In this article, a distributed multiobjective optimization problem is formulated for the resource allocation of network-connected multiagent systems. The framework encompasses a group of distributed decision makers in the subagents, where each of them possesses a local preference index. Novel distributed algorithms are proposed to solve such a problem in a distributed manner. The weighted L-p preference index is utilized in each agent since it can provide a robust Pareto solution to the problem. By using distributed fixed-time optimization methods, the L-p preference index is constructed online without specifying the unknown parameters. Then, it is proved that the problem admits a unique Pareto solution. By exploiting consensus and gradient descent techniques, asymptotic convergence to the optimal solution is established via Lyapunov theories. Distinct from most of the current works, the proposed framework does not require any prior information in the formulation process, and private data can be well protected using this distributed approach. Numerical examples are included to validate the effectiveness of the proposed algorithms.

关键词： distributed algorithms multiagent systems multiobjective optimization resource allocation

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Adaptive Geman-McClure Estimator for Robust distributed Channel Estimation

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IEEE ACCESS 2021年 9卷 93691-93702页

作者： Wilson, Annet Mary Panigrahi, Trilochan Mishra, Bishnu Prasad Sabat, Samrat L. Natl Inst Technol Goa Dept Elect & Commun Engn Farmagudi 403401 Goa India Univ Hyderabad Ctr Adv Studies Elect Sci & Technol Sch Phys Hyderabad 500046 India

Communication systems are affected by channel distortions. Impulsive noise is one of the significant factors for channel impairments. The standard additive white Gaussian noise (AWGN) channel model and conventional estimation algorithms like least mean square (LMS) and its variants tend to be ineffective under such conditions. This paper presents a robust adaptive channel estimation algorithm using the Geman-McClure estimator in a diffusion-based distributed network. The analytical study on mean stability and mean square analysis is carried out under two separate noise statistics: Symmetric alpha-stable (S alpha S) and Bernoulli-Gaussian (BG) distribution. The computer simulations confirm the proposed algorithm's competitive robustness compared to the Maximum Correntropy Criterion and Minimum Kernel Risk Sensitive Loss algorithms at a high impulsive noise environment without exponential cost function. Further, the efficiency is also verified by simulating the bit error rate by designing a minimum mean square error (MMSE) equalizer with the estimated coefficients.

关键词： Channel estimation diffusion cooperation distributed algorithms Geman-McClure robust estimation wireless sensor networks

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distributed Spectrum Sharing for Enterprise Powerline Communication Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2021年第3期29卷 1032-1045页

作者： Ali, Kamran Liu, Alex X. Pefkianakis, Ioannis Kim, Kyu-Han Gen Motors Res & Dev Warren MI 48557 USA Qilu Univ Technol Jinan 250353 Peoples R China Apple Cupertino CA 95014 USA Hewlett Packard Labs Networking & Mobil Grp Palo Alto CA 94304 USA

As powerline communication (PLC) technology does not require dedicated cabling and network setup, it can be used to easily connect multitude of IoT devices deployed in enterprise environments for sensing and control related applications. IEEE has standardized the PLC protocol in IEEE 1901, also known as HomePlug AV (HPAV) which has been widely adopted in mainstream PLC devices. A key weakness of HPAV protocol is that it does not support spectrum sharing. Currently, each link in an HPAV PLC network operates over the whole available spectrum, and only one link can operate at any time within a single collision domain. In this work, through an extensive measurement study of HPAV PLCs in a real enterprise environment using commodity off-the-shelf (COTS) HPAV PLC devices, we discover that spectrum sharing can significantly benefit enterprise level PLC networks. To this end, we propose a distributed spectrum sharing technique for enterprise HPAV PLC networks, and show that fine-grained distributed spectrum sharing on top of current HPAV MAC protocols can significantly boost the aggregated and per-link throughput, by allowing multiple PLC links to communicate concurrently, while requiring only a few modifications to the existing HPAV devices and protocols.

关键词： Throughput Protocols Logic gates Channel estimation Time division multiple access Mesh networks Media Access Protocol Computer networks Internet of Things channel state information distributed algorithms

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A Fixed-time distributed Algorithm for Least Square Solutions of Linear Equations

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2021年第3期19卷 1311-1318页

作者： Li, Shuling Ai, Wu Wu, Jian Feng, Quanxi Guilin Univ Technol Coll Sci Guilin 541004 Peoples R China Guilin Univ Technol Ctr Data Anal & Algorithm Technol Guilin 541004 Peoples R China Anqing Normal Univ Sch Comp & Informat Anqing 246011 Peoples R China

This study presents a fixed-time convergent algorithm to achieve distributed least square (DLS) solutions of networked linear equations. Each agent in the network only knows a subset of the equations and can only exchange messages with its nearest neighbors. Unlike finite-time counterparts, the settling time of the fixed-time distributed algorithm does not depend upon the initial states, and can be preassigned according to the requirements of the task. Numerical simulations verify the theoretical results.

关键词： distributed algorithms fixed-time stability least squares linear equations

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